Functional magnetic resonance imaging (fMRI) as an imaging modality for recording the human brain activities with 4-dimensional (3-D space+1-D time) signals plays an increasingly important role in both basic and clinical research studies of the brain function. Based on the fMRI data collected at resting-state, the brain functional connectivity is typically investigated using regional correlation analysis based approaches or independent component analysis (ICA) methods. The former approaches estimate the brain connectivity by computing correlation measures of functional temporal signals between regions of interest (ROIs) or correlating functional temporal signals of a seed region to those of other brain voxels. The selection of ROIs typically requires a priori knowledge about a problem under study. In contrast to the regional correlation analysis methods, the ICA approach is a data driven technique. For brain network analysis, the ICA, particularly spatial ICA, is used to discover spatially independent components (ICs), each of which encodes temporally coherent brain regions. The temporally coherent brain regions encoded by each individual IC are often referred to as a functional network.
For ICA studies of multiple subjects, the spatial ICA is typically applied to concatenated group imaging data formed by concatenating imaging data from all subjects in the temporal dimension, and subject specific ICs are then obtained by back reconstruction or dual regression. In most ICA studies, the number of ICs is typically set to 20, although it can also be estimated somehow (Calhoun, V. D., Liu, J., Adali, T., 2009. “A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data”, Neuroimage, 45, pp S163-S172).
The ICA as a subspace analysis technique has also been widely applied in multivariable data representation with each IC as a basis function for spanning the data subspace. For the subspace data representation based on ICA, different combinations of basis functions (ICs) can be used to capture information of the data with distinctive purposes. For instance, fMRI data's signal to noise ratio can be improved in the subspace data representation by removing ICs corresponding to imaging noise. Similarly, a combination of some ICs, if they are more closely related to the brain function under study, might better capture the brain functional activities from fMRI brain data than combinations of other ICs. In the method proposed in Fan et al. (Y. Fan, Y. Liu, H. Wu, Y. Hao, H. Liu, Z. Liu, T. Jiang, 2011. “Discriminant analysis of functional connectivity patterns on Grassmann manifold”, Neuroimage, 56, pp. 2058-2067), the ICs of each individual subject's fMRI data were used as basis functions of a linear subspace, referred to as a brain spatial functional connectivity pattern (FCP), and a corresponding pattern similarity a difference measure is proposed. The functional connectivity pattern method may capture the difference between different brain active levels or status.
Many studies have demonstrated that correlation measures between functional signals of two brain regions can capture their functional connectivity information. However, no method has been proposed to simultaneously characterize the spatial functional connectivity pattern from fMRI data based on ICA and correlation measures among time courses (TC) of different ICs, referred to as temporal functional connectivity pattern.